Chromatone Visual Music Theory Knowledge Graph ​

A semantic knowledge graph for visual music theory, built with MD-LD (Markdown-Linked Data). Chromatone is based on a scientific mapping of the 12 chromatic notes of the octave to the 12 main hues of the color wheel. This allows you to visualize music, making it easier to understand how it works, start improvising on instruments, and compose complete pieces from scratch. Website: https://chromatone.center/Repository: https://github.com/chromatoneEstablished: 2018 Maintained by: Denis Starov

Knowledge Graph Structure ​

This knowledge graph organizes visual music theory into interconnected domains:

Core Ontology Layer ​

The foundational vocabulary defining all concepts in Chromatone:

  • Classes: ColorNote, PitchClass, ColorInterval, ColorChord, ColorScale, RhythmShape
  • Properties: hasHueAngle, hasColorHex, hasSemitoneIndex, hasFrequency, formsPalette
  • Mappings: pitch-to-color, interval-to-gradient, chord-to-palette, rhythm-to-shape See: Chromatone Core Vocabulary

Domain Modules ​

1. Introduction Module ​

Foundation concepts and course orientation:

  • Welcome and Vision
  • Chromatone System Overview
  • Course Structure and Learning Path
  • Technical Setup and Tools
  • Chromatic Instruments
  • Getting Started Guide

2. Color & Light Module ​

Physics of light and color perception:

  • Light, Sun, Electromagnetic Waves
  • Color, Physiology of Perception
  • Color Models and Working with Them
  • Color Wheel, Primary Colors

3. Sound & Physics Module ​

Acoustic foundations and psychoacoustics:

  • Physical Nature of Sound
  • Human Auditory System, Psychoacoustics
  • Timbre from Physics and Harmonics
  • Consonance and Dissonance
  • Tuning Systems

4. Notes & Notation Module ​

Music notation systems and Chromatone notation:

  • Introduction to Music Notation
  • Methods of Writing Notes, History
  • Music Staff, Notes, Keys, Alterations
  • Alphabetic and Numerical Notation
  • Letter and Color Designations in Chromatone

5. Intervals Module ​

Musical intervals and their visual representations:

  • Interval, Types, Tones, and Semitones
  • Octave/Unison (Prima) 1:1
  • Fifth 2:3, Fourth 3:4, Circle of Fifths
  • Thirds
  • Seconds and Sevenths

6. Chords Module ​

Chord theory and construction:

  • What is a Chord, Types, Quintaccord
  • Triads
  • Seventh Chords
  • Chords of 5 or More Notes

7. Scales and Modes Module ​

Modal theory and scale construction:

  • Mode, Tonality, and Scale
  • Stages of Modes, Diatonic and Pentatonic
  • Major, Minor, and Natural Modes
  • Oriental, Blues, and Symmetrical Modes

8. Beat, Tempo, Rhythm Module ​

Rhythmic foundations and patterns:

  • Pulsation and Rhythm
  • Tempo, Beat, and Downbeat
  • Duration
  • Pause, Accent, Syncopation, Swing, and Groove
  • Meters

9. Melody Module ​

Melodic construction and development:

  • Melody and Its Meaning in Music
  • Types of Movements in Melody
  • Motif, Phrase, and Basic Principles
  • Basic Techniques for Constructing a Phrase
  • Theme, Sentence, and Period
  • Creating a Melody on a Musical Instrument

10. Harmony Module ​

Harmonic theory and progressions:

  • Harmony and Its Meaning in Music
  • Functions of Chords and Chord Progressions
  • Popular Chord Progressions
  • Accompaniment

11. Composition Module ​

Compositional techniques and forms:

  • Composition and Dynamics
  • Concept Forms in Music
  • Basic Compositional Techniques, Verse-Chorus
  • Bridge, Solo, Drop, Modulation, Improvisation

Ontology Foundations ​

This knowledge graph uses standard W3C ontologies extended with Chromatone-specific vocabulary:

Core RDF Layer ​

  • RDF — Resource Description Framework (triples, statements, containers)
  • RDFS — RDF Schema (classes, properties, hierarchies)
  • XSD — XML Schema Datatypes (typed literals: date, integer, decimal, boolean)

Provenance & Validation ​

  • PROV-O — Provenance ontology (entities, activities, agents, attribution)
  • SHACL — Shapes Constraint Language (validation rules, data constraints)

Metadata & Discovery ​

  • DCTERMS — Dublin Core Terms (titles, creators, dates, subjects)
  • FOAF — Friend of a Friend (people, organizations, relationships)
  • Schema.org — Web vocabulary (courses, lessons, educational content)

Chromatone Extensions ​

Domain-specific classes and properties for visual music theory:

  • Color Mapping — hasHueAngle, hasColorHex, colorOf, noteOf
  • Interval Theory — hasSemitoneSpan, hasIntervalGradient, hasConsonanceVisual
  • Chord/Scale Theory — formsPalette, hasMemberNote, hasRootNote, hasInversion
  • Rhythm Visualization — hasRhythmShape, hasDurationVisual, hasBeatPattern
  • Educational Scaffolding — CourseModule, LessonUnit, AssessmentItem, MasteryStatement See: Chromatone Core Vocabulary

Knowledge Statements Examples ​

Foundational facts expressed as reified knowledge statements:

Pitch-Color Mappings ​

Statement: A maps to Red Subject: Pitch Class A Predicate: hasColorHex Object: #FF0000 Confidence: 1.0 Evidence: Octave bridge physics: A4=440Hz × 2^40 ≈ red light frequency

Interval Relationships ​

Statement: Perfect Fifth = 7 semitones Subject: Perfect Fifth Interval Predicate: hasSemitoneSpan Object: 7 Frequency Ratio: 2:3Statement: Circle of Fifths orders all 12 pitch classes Subject: Circle of Fifths Predicate: orders Object: All 12 Pitch Classes Visual Property: Clockwise arrangement by perfect fifths

Chord Construction ​

Statement: Major triad = root + major third + minor third Subject: Major Triad Predicate: hasIntervalStructure Object: 0, 4, 7 semitones Components:

  • Root
  • Major Third (4 semitones)
  • Perfect Fifth (7 semitones)

Educational Scaffolding ​

Learning Objectives ​

Students will be able to:

  1. Identify pitch-color mappings — Recognize all 12 notes by their Chromatone colors
  2. Visualize intervals — See interval quality through color gradients and distances
  3. Construct chords visually — Build chord palettes from interval patterns
  4. Understand consonance/dissonance — Relate acoustic phenomena to visual smoothness/clash
  5. Apply rhythm shapes — Translate temporal patterns to geometric forms
  6. Compose with color — Create musical pieces using visual-spatial reasoning

Assessment Framework ​

Assessment items test specific knowledge statements: Sample Assessment: Identify the interval Tests: Perfect Fifth identification Difficulty: 0.3 Estimated Time: PT2M Format: Multiple choice with color gradient visualization

Prerequisite Chains ​

Learning paths with dependency tracking: Understanding Intervals requires:

  • [Pitch Classes]
  • [Semitone counting] Understanding Chords requires:
  • [Intervals]
  • [Triad construction]

Applications & Tools ​

Interactive web applications implementing visual music theory:

Practice Apps ​

  • Chroma Piano — Real-time MIDI input with chromatone color feedback
  • Interval Trainer — Drill interval recognition using color gradients
  • Chord Palette Builder — Construct and explore chord color palettes
  • Rhythm Shape Explorer — Visualize rhythmic patterns as geometric forms

Visualization Tools ​

  • Spectrogram View — Real-time frequency analysis with chromatone mapping
  • Circle of Fifths Interactive — Explore key relationships visually
  • Tonnetz Grid — 2D lattice navigation of harmonic space

Physical Materials ​

  • Chromatone Stickers — Color-coded instrument labels for piano, guitar, ukulele
  • Printed Reference Charts — Wall posters of pitch-color mappings, interval tables All apps support:
  • MIDI input/output: true
  • Multi-touch: true
  • Offline capability: true

Provenance & Versioning ​

Document History ​

Initial Knowledge Graph Construction Started: 2026-01-01 Agent: Denis Starov Generated: Chromatone VMT Knowledge Graph v1.0 Used: MD-LD Specification Used: Chromatone Core Vocabulary

Version Information ​

Current Version: 1.0.0 License: Open source, non-commercial Repository: https://github.com/chromatone/vmt-kg ​

Next Steps for Growth ​

Phase 1: Foundation (Q1 2026) ​

  • ✅ Define core ontology classes and properties
  • ✅ Model 12 pitch classes with hue angles and color hex values
  • ⬜ Encode all basic intervals (P1, m2, M2, m3, M3, P4, tritone, P5, m6, M6, m7, M7, P8)
  • ⬜ Create lesson units for all 11 modules

Phase 2: Expansion (Q2 2026) ​

  • ⬜ Add all triad types (major, minor, augmented, diminished)
  • ⬜ Model seventh chords and extended harmonies
  • ⬜ Encode all modes (Ionian, Dorian, Phrygian, Lydian, Mixolydian, Aeolian, Locrian)
  • ⬜ Create assessment items for each knowledge statement

Phase 3: Applications (Q3 2026) ​

  • ⬜ Link interactive apps to theory concepts
  • ⬜ Add practice exercise graphs
  • ⬜ Implement student portfolio tracking
  • ⬜ Build teacher feedback system

Phase 4: Community (Q4 2026) ​

  • ⬜ Enable community contributions via MD-LD merge workflows
  • ⬜ Publish SHACL validation shapes for data quality
  • ⬜ Create multilingual versions (@en, @es, @fr, @de, @ja)
  • ⬜ Integrate with external music theory ontologies

How to Use This Knowledge Graph ​

For Learners ​

Browse modules sequentially or jump to specific topics. Each lesson links to:

  • Theory articles explaining concepts
  • Interactive apps for hands-on practice
  • Assessment items to test understanding

For Developers ​

Use the structured data to:

  • Build new educational applications
  • Validate curriculum completeness
  • Generate practice exercises programmatically
  • Create adaptive learning pathways

For Researchers ​

Extend the ontology with:

  • New domain-specific classes and properties
  • Cross-references to academic music theory
  • Empirical studies on visual music learning
  • Cultural variations in pitch-color associations

Technical Implementation ​

This knowledge graph is authored in MD-LD (Markdown-Linked Data), enabling:

  • Human-readable — Plain Markdown with semantic annotations
  • Machine-parseable — Extracts to RDF quads compatible with n3.js, rdflib
  • Round-trip safe — Parse → generate → parse preserves semantics
  • Merge-friendly — CRDT-style document merging for collaboration
  • Zero dependencies — Pure JavaScript, 86KB unminified Parse with:
javascript
import { parse } from 'mdld-parse';
const result = parse({ text: mdldContent });
console.log(result.quads); // RDF/JS quads

Generate from quads:

javascript
import { generate } from 'mdld-parse';
const { text } = generate({ quads: myQuads });

See: MD-LD Documentation ​

References ​

This knowledge graph grows iteratively. Each contribution adds semantic depth while maintaining human readability.